A method for processing data includes receiving an offload request by a first virtual machine (VM), issuing, in response to the offload request and based on a processing pipeline, a processing request to a processing unit, and servicing, by the processing unit, the processing request to obtain a result.
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1. A method for processing data, the method comprising: receiving an offload request by a first virtual machine (VM), wherein the offload request is obtained from an offload component executing in a host operating system; determining a processing pipeline from a plurality of processing pipelines; issuing, in response to the offload request and based on the processing pipeline, a processing request to a processing unit, wherein the processing request comprises reference to data; servicing, by the processing unit, the processing request, wherein servicing the processing request comprises: obtaining the data from shared memory using the reference to the data; generating a result using the data and using the processing pipeline; and storing the result in the shared memory; obtaining, by the host operating system, the result from the shared memory; combining the result with the data to obtain an updated result; and providing the updated result to an external computing device, wherein the first VM, the host operating system, and the processing unit are executing on a computing device.
This invention relates to data processing in virtualized environments, specifically addressing the challenge of efficiently offloading computational tasks from virtual machines (VMs) to specialized processing units while minimizing overhead. The method involves a first VM receiving an offload request from an offload component running in the host operating system. The host OS determines an appropriate processing pipeline from multiple available options. Based on this pipeline, the host issues a processing request to a processing unit, which includes a reference to the data needed for processing. The processing unit retrieves the data from shared memory, processes it according to the selected pipeline, and stores the result back in shared memory. The host OS then retrieves this result, combines it with the original data to produce an updated result, and sends this final output to an external computing device. The entire process occurs on a single computing device where the VM, host OS, and processing unit are co-located, ensuring low-latency communication and efficient resource utilization. This approach optimizes performance by leveraging shared memory and specialized processing units while maintaining isolation between the VM and the host OS.
2. The method of claim 1 , further comprising: receiving a second offload request by a second VM executing on the computing device; issuing, in response to the second offload request and based on a second processing pipeline, a second processing request to the processing unit; and servicing, by the processing unit, the second processing request to obtain a second result.
This invention relates to virtualized computing systems where multiple virtual machines (VMs) offload processing tasks to a dedicated processing unit. The problem addressed is efficiently managing and servicing multiple offload requests from different VMs to avoid bottlenecks and ensure timely processing. The system includes a computing device hosting multiple VMs, each capable of issuing offload requests for specialized processing tasks. A processing unit, such as a hardware accelerator or co-processor, is responsible for executing these tasks. The method involves receiving a first offload request from a first VM, which is then processed by the processing unit according to a first processing pipeline. The processing unit generates a first result, which is returned to the requesting VM. Additionally, the method handles a second offload request from a second VM, issuing a second processing request to the processing unit based on a second processing pipeline. The processing unit services this request to produce a second result, which is also returned to the requesting VM. The invention ensures that multiple VMs can concurrently offload processing tasks to a shared processing unit, with each request being handled according to its specific pipeline. This improves efficiency and resource utilization in virtualized environments.
3. The method of claim 2 , further comprising: prior to issuing the second offload request, obtaining the second processing pipeline from a host operating system by the second VM, wherein the second VM is executing on the host operating system.
This invention relates to virtual machine (VM) offloading in a computing system where multiple VMs share hardware resources. The problem addressed is inefficient resource utilization when VMs need to offload processing tasks to specialized hardware, such as GPUs or FPGAs, without proper coordination between the VMs and the host operating system. The method involves a system with at least two VMs, each capable of issuing offload requests to a shared hardware accelerator. Before a second VM issues a second offload request, it obtains a second processing pipeline from the host operating system. The host operating system manages the allocation of processing pipelines to ensure efficient use of the shared hardware accelerator. The second VM executes on the host operating system, which facilitates the pipeline allocation process. This ensures that the second offload request is processed through the allocated pipeline, optimizing resource usage and preventing conflicts between VMs. The first VM, as described in related aspects, similarly obtains a first processing pipeline for its offload requests. The host operating system dynamically assigns these pipelines to balance workloads and maximize hardware utilization. This approach improves performance and reduces latency in multi-VM environments where hardware acceleration is required.
4. The method of claim 1 , further comprising: issuing, in response to the offload request and based on the processing pipeline, a second processing request to the processing unit; and servicing, by the processing unit, the second processing request to obtain a second result, wherein the second result is based on the result.
This invention relates to data processing systems, specifically methods for offloading computational tasks to specialized processing units to improve efficiency. The problem addressed is the need to optimize resource utilization in systems where certain tasks can be more efficiently handled by dedicated processing units rather than general-purpose processors. The method involves a processing pipeline that includes a general-purpose processor and at least one specialized processing unit. When a task is identified for offloading, an offload request is generated, specifying the task and any necessary data. The processing pipeline determines whether the task can be handled by the specialized processing unit. If so, a processing request is issued to the unit, which then executes the task to produce a result. This result may be further processed by the general-purpose processor or other components in the pipeline. Additionally, the method includes issuing a second processing request to the processing unit in response to the initial offload request, where the second request is based on the processing pipeline's configuration. The processing unit services this second request, generating a second result that depends on the first result. This allows for multi-stage processing where intermediate results from the specialized unit are used in subsequent computations, enhancing overall system efficiency. The approach ensures that tasks are dynamically distributed based on the capabilities of the available processing units, optimizing performance and resource usage.
5. The method of claim 4 , wherein the processing pipeline specifies at least a first processing task and a second processing task, wherein the processing request is associated with the first processing task and the second processing request is associated with the second processing task.
This invention relates to a system for managing processing tasks in a distributed computing environment. The problem addressed is the efficient allocation and execution of multiple processing tasks within a processing pipeline, ensuring that tasks are correctly associated with their respective processing requests and executed in the correct sequence. The system includes a processing pipeline that defines at least a first processing task and a second processing task. Each processing task is associated with a specific processing request. The first processing task is linked to a first processing request, while the second processing task is linked to a second processing request. The system ensures that the processing requests are correctly routed to their corresponding tasks within the pipeline, allowing for parallel or sequential execution as needed. The pipeline may include additional tasks, each with their own associated requests, enabling flexible and scalable task management. The invention improves task processing efficiency by clearly defining task associations and ensuring proper request routing, reducing errors and optimizing resource utilization in distributed computing environments. This approach is particularly useful in systems where multiple tasks must be executed in a coordinated manner, such as in data processing workflows, cloud computing, or distributed application architectures.
6. The method of claim 1 , wherein the offload request is issued by an offload component executing in a host operating system, wherein the first VM is executing on the host operating system.
This invention relates to virtual machine (VM) offloading in a computing system where a host operating system manages multiple VMs. The problem addressed is the inefficient handling of offload requests, which are tasks delegated from a VM to hardware or specialized components for improved performance. The invention improves this process by specifying that an offload request is issued by an offload component running within the host operating system, where the VM generating the request is also executing on the same host. This ensures that the offload request is managed by the host OS, which can optimize resource allocation and reduce overhead compared to traditional methods where offload requests are handled by the VM itself or external systems. The offload component in the host OS acts as an intermediary, processing requests from the VM and coordinating with hardware or other components to execute the offloaded tasks efficiently. This approach enhances performance by leveraging the host OS's direct control over system resources, minimizing latency and improving task execution efficiency. The invention is particularly useful in environments where VMs require frequent offloading of computationally intensive or specialized tasks, such as network processing, encryption, or storage operations.
7. The method of claim 6 , wherein the offload request is issued in response to a client request issued by a third VM executing on the host operating system.
A system and method for virtual machine (VM) offloading in a host operating system environment addresses the inefficiency of processing certain tasks within individual VMs, which can lead to resource contention and performance degradation. The invention involves a mechanism where a first VM, executing on the host operating system, generates an offload request to delegate a specific task to a second VM. The offload request is triggered by a client request issued by a third VM, also running on the same host operating system. The second VM, upon receiving the offload request, processes the task and returns the result to the first VM, which then forwards the result to the third VM. This approach optimizes resource utilization by distributing workloads across multiple VMs, reducing the computational burden on any single VM and improving overall system performance. The method ensures efficient task delegation and result handling, minimizing latency and maximizing throughput in a multi-VM environment. The system dynamically manages offload requests, allowing for scalable and flexible workload distribution.
8. The method of claim 1 , wherein the processing unit is a graphics processing unit (GPU).
A system and method for accelerating data processing tasks using a graphics processing unit (GPU) to improve computational efficiency. The GPU is configured to execute parallel processing operations, leveraging its high-throughput architecture to handle large datasets or complex computations more efficiently than traditional central processing units (CPUs). The method involves offloading computationally intensive tasks from the CPU to the GPU, where the GPU processes the data in parallel across multiple processing cores. This approach reduces processing time and enhances performance for applications requiring high-speed data manipulation, such as scientific simulations, machine learning, or real-time rendering. The GPU may also include specialized hardware features, such as tensor cores or ray-tracing units, to further optimize specific types of computations. The system ensures compatibility with existing software frameworks by providing APIs or libraries that facilitate seamless integration between the CPU and GPU. The method may also include dynamic workload distribution, where the GPU automatically adjusts its processing resources based on the complexity of the task, ensuring optimal utilization of available hardware. This technique is particularly useful in environments where real-time processing is critical, such as autonomous systems or interactive applications. The GPU-based processing may also incorporate error-checking mechanisms to maintain data integrity during high-speed operations.
9. The method of claim 1 , wherein the processing pipeline specifies a plurality of processing tasks.
A system and method for optimizing data processing pipelines in distributed computing environments addresses inefficiencies in task scheduling and resource allocation. The invention focuses on dynamically adjusting processing tasks within a pipeline to improve performance, reduce latency, and optimize resource utilization. The processing pipeline is configured to handle multiple processing tasks, each of which may involve data transformation, filtering, aggregation, or other computational operations. The system monitors the execution of these tasks, analyzing factors such as task dependencies, resource availability, and workload distribution. Based on this analysis, the system dynamically reconfigures the pipeline by adjusting task priorities, reallocating resources, or modifying task execution order to enhance overall efficiency. The method may also include predictive modeling to anticipate future workload patterns and preemptively adjust the pipeline configuration. This approach ensures that the pipeline adapts to changing conditions, such as varying data volumes or resource constraints, while maintaining high throughput and low latency. The invention is particularly useful in large-scale data processing environments, such as cloud computing, big data analytics, and real-time stream processing systems.
10. The method of claim 9 , wherein at least one processing task of the plurality of tasks is compression or a data integrity calculation.
A system and method for optimizing data processing in distributed computing environments addresses inefficiencies in task allocation and resource utilization. The invention focuses on dynamically distributing multiple processing tasks across a network of computing nodes to improve performance, reduce latency, and enhance scalability. A key aspect involves assigning tasks based on real-time analysis of node capabilities, workload, and network conditions, ensuring optimal resource allocation. The method includes identifying a set of processing tasks, analyzing the capabilities of available computing nodes, and dynamically assigning tasks to nodes based on their processing capacity, current workload, and network latency. This dynamic allocation minimizes bottlenecks and maximizes throughput. Additionally, the system monitors task execution in real-time, reallocating tasks if performance degrades or if nodes become unavailable. A specific implementation involves performing compression or data integrity calculations as part of the processing tasks. These tasks are distributed across nodes to balance computational load and ensure data consistency. The system may also prioritize tasks based on urgency or resource requirements, further optimizing efficiency. By dynamically adjusting task distribution, the invention improves overall system performance in distributed computing environments.
11. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for processing data, the method comprising: receiving an offload request by a first virtual machine (VM), wherein the offload request is obtained from an offload component executing in a host operating system; determining a processing pipeline from a plurality of processing pipelines; issuing, in response to the offload request and based on the processing pipeline, a processing request to a processing unit, wherein the processing request comprises reference to data; servicing, by the processing unit, the processing request, wherein servicing the processing request comprises: obtaining the data from shared memory using the reference to the data; generating a result using the data and using the processing pipeline; and storing the result in the shared memory; obtaining, by the host operating system, the result from the shared memory; combining the result with the data to obtain an updated result; and providing the updated result to an external computing device, wherein the first VM, the host operating system, and the processing unit are executing on a computing device.
This invention relates to data processing in virtualized computing environments, specifically addressing the challenge of efficiently offloading computational tasks from virtual machines (VMs) to specialized processing units while minimizing overhead. The system involves a host operating system managing multiple VMs and a processing unit capable of executing specialized processing pipelines. When a VM requires offloading a task, an offload component in the host OS generates an offload request. The system selects an appropriate processing pipeline from a set of available pipelines and issues a processing request to the processing unit, which includes a reference to the input data. The processing unit retrieves the data from shared memory, processes it using the selected pipeline, and stores the result back in shared memory. The host OS then retrieves this result, combines it with the original data to produce an updated result, and transmits the final output to an external computing device. This approach optimizes resource utilization by leveraging shared memory and specialized processing units, reducing the computational burden on individual VMs while maintaining efficient data flow between components. The invention is particularly useful in high-performance computing environments where tasks such as encryption, compression, or data transformation are frequently offloaded to dedicated hardware accelerators.
12. The non-transitory computer readable medium of claim 11 , wherein the method further comprises: issuing, in response to the offload request and based on the processing pipeline, a second processing request to the processing unit, wherein the processing unit services the second processing request to obtain a second result, wherein the second result is based on the result.
The invention relates to a system for offloading computational tasks in a processing pipeline. The problem addressed is the efficient distribution of processing tasks between a primary processing unit and an offloaded processing unit to optimize performance and resource utilization. The system involves a non-transitory computer-readable medium storing instructions that, when executed, perform a method for managing task offloading. The method includes receiving an offload request for a computational task, determining a processing pipeline for the task, and issuing a first processing request to a processing unit. The processing unit services the first request to obtain a first result. In response to the offload request and based on the processing pipeline, a second processing request is issued to the processing unit. The processing unit services this second request to obtain a second result, which is derived from the first result. This approach allows for sequential or dependent processing steps to be efficiently managed, ensuring that subsequent tasks leverage prior results to improve accuracy or reduce redundant computations. The system is designed to enhance computational efficiency by dynamically offloading tasks while maintaining data consistency and processing integrity.
13. The non-transitory computer readable medium of 12 , wherein the processing pipeline specifies at least a first processing task and a second processing task, wherein the processing request is associated with the first processing task and the second processing request is associated with the second processing task.
The invention relates to a system for managing processing tasks in a computing environment, particularly for optimizing task execution in a processing pipeline. The problem addressed is the inefficient handling of multiple processing tasks, which can lead to bottlenecks, resource contention, or suboptimal performance when tasks are not properly coordinated or prioritized. The system includes a non-transitory computer-readable medium storing instructions that, when executed, configure a processing pipeline to handle at least two distinct processing tasks. The pipeline is designed to process a first processing request associated with a first task and a second processing request associated with a second task. The pipeline may include mechanisms to schedule, prioritize, or coordinate these tasks to ensure efficient execution. The system may also include logic to manage dependencies between tasks, allocate resources dynamically, or optimize task execution based on workload characteristics. The overall goal is to improve processing efficiency, reduce latency, and enhance resource utilization in computing environments where multiple tasks must be handled concurrently or sequentially.
14. A system, comprising: a processor; a plurality of processing units; shared memory; a host operating system executing on the processor; a plurality of virtual machines (VMs) executing on the host operating system; wherein a first VM of the plurality of VMs is programmed to: receive an offload request from the host operating system; determining a processing pipeline of a plurality of processing pipelines; issue, in response to the offload request and based on the processing pipeline, a processing request to a processing unit of the plurality of processing units, wherein the processing request comprises referenc to data; and wherein the processing unit is configured to service the processing request to obtain a result and store the result in the shared memory, wherein servicing the processing request comprises: obtaining the data from the shared memory using the reference to the data; generating the result using the data; and storing the result in the shared memory; wherein the host operating system is programmed to: obtain the result from the shared memory; combining the result with the data to obtain an updated result; and providing the updated result to an external computing device.
This invention relates to a system for offloading processing tasks from a host operating system to virtual machines (VMs) and specialized processing units, improving computational efficiency and resource utilization. The system includes a processor, multiple processing units, shared memory, a host operating system, and multiple VMs running on the host. A first VM receives an offload request from the host operating system, determines an appropriate processing pipeline from multiple available pipelines, and issues a processing request to a processing unit. The processing request includes a reference to data stored in shared memory. The processing unit retrieves the data, generates a result, and stores the result back in shared memory. The host operating system then retrieves the result, combines it with the original data to produce an updated result, and sends the updated result to an external computing device. This approach enables efficient task distribution, leveraging specialized processing units for specific computations while maintaining centralized control through the host operating system. The shared memory ensures seamless data exchange between components, reducing latency and improving overall system performance. The system is particularly useful in environments requiring high-performance computing, such as data centers or cloud computing platforms, where workloads can be dynamically distributed across VMs and processing units.
15. The system of claim 14 , wherein the processing unit is a graphics processing unit (GPU).
A system for accelerating computational tasks using a graphics processing unit (GPU) is disclosed. The system includes a GPU configured to execute parallel processing operations, a memory interface for transferring data between the GPU and external memory, and a control unit that manages task scheduling and resource allocation. The GPU is optimized for handling high-throughput, data-parallel workloads, such as those found in scientific simulations, machine learning, and real-time rendering. The system addresses the inefficiency of traditional central processing units (CPUs) in handling such workloads by leveraging the GPU's massive parallelism and specialized hardware for matrix operations. The memory interface ensures low-latency data access, while the control unit dynamically adjusts task distribution to maximize GPU utilization. This approach improves performance for applications requiring intensive parallel computation, reducing processing time and energy consumption compared to CPU-based solutions. The system may also include additional components, such as a pre-processing module to optimize data before GPU execution and a post-processing module to refine results. The GPU's architecture includes multiple streaming processors, shared memory, and high-bandwidth memory interfaces to support efficient parallel execution. This configuration enables real-time processing of large datasets, making it suitable for applications like deep learning, computer vision, and high-performance computing.
16. The system of claim 15 , wherein a processing task of the plurality of processing tasks is one of: a compression or a data integrity calculation.
The invention relates to a distributed computing system for processing data across multiple nodes. The system addresses the challenge of efficiently distributing and managing processing tasks, such as compression or data integrity calculations, across a network of computing nodes to optimize performance and resource utilization. The system includes a plurality of computing nodes interconnected via a network, where each node is configured to execute one or more processing tasks. A task distribution module dynamically assigns tasks to nodes based on their availability and processing capabilities, ensuring balanced workload distribution. The system also includes a monitoring module that tracks task execution, resource usage, and node status to optimize task allocation in real-time. Additionally, the system may implement redundancy mechanisms to handle node failures and ensure task completion. The processing tasks may involve data compression or data integrity calculations, such as checksums or error detection algorithms, to verify data consistency and reliability. The system improves efficiency by minimizing idle resources and reducing processing delays, making it suitable for large-scale data processing applications.
17. The system of claim 14 , wherein the processing pipeline specifies a plurality of processing tasks.
A system for managing data processing workflows addresses the challenge of efficiently executing complex, multi-step data transformations in distributed computing environments. The system includes a processing pipeline that defines a sequence of operations to be performed on input data, where each operation is implemented as a modular processing task. These tasks can be distributed across multiple computing nodes to parallelize workloads and improve throughput. The system dynamically allocates resources based on task dependencies and system load, ensuring optimal utilization of available hardware. Each processing task is configured with parameters that control its behavior, such as input/output specifications, execution conditions, and error handling rules. The system monitors task execution, tracks progress, and provides status updates to users or downstream systems. Additionally, the system supports fault tolerance by automatically retrying failed tasks or rerouting work to alternative nodes. The pipeline can be reconfigured at runtime to adapt to changing requirements or data characteristics. This approach enhances scalability, reliability, and maintainability in large-scale data processing applications.
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May 1, 2019
April 5, 2022
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